About the role

Data Scientist developing next generation surveillance solutions for Fidelity's financial crime detection. Engaging in various projects on AML typologies and collaboration across teams.

Responsibilities

research, develop, and deliver next generation surveillance solutions on a wide range of AML typologies
Design and tune both machine learning and rules-based solutions for the Fidelity Digital Assets business
Research and develop models that identify suspicious transactions and customers
Contribute to implementation of LLM-powered solutions in support of the greater Financial Crimes Compliance organization
Work on multiple long/medium-term data science projects concurrently under moderate direction
Participate in code reviews to enable learning, collaboration and mentoring of other team members
Make presentations to update team on project progress, research and new findings
Collaborate with members of the team as well as external teams on the planning, research, development and productizing of data science solutions
Stay current with advances in ML/AI, especially in the areas of cryptocurrency, generative AI and financial crime detection
Document research findings and project progress

Bachelor’s in Computer Science, Mathematics, Computational Statistics or related field and several years of related experience or a Master’s degree in a related field
Strong programming skills including 2+ years’ experience with Python and SQL
Experience carrying out various aspects of a data science project including exploratory analysis, data cleaning, preparation and annotation, ML pipeline design and development, model evaluation and validation
Experience with LLM frameworks and tools (e.g., LangChain, deepeval)
Familiarity with RAG architectures, prompt engineering, and fine-tuning techniques
Experience with libraries such as Spacy, NLTK, Stanford NER, scikit-learn, pandas, tensorflow, keras, pytorch, numpy
Experience with big data tools such as Spark or snowpark
Experience working with smaller data sets and a lack of labeled data
Familiarity with digital assets
Proven experience with both supervised and unsupervised machine learning algorithms such as decision trees, isolation forests, autoencoders/neural networks, linear/logistic regression, clustering, etc
Experience with general software tools/frameworks such as git, pytest, dbt
Experience with most of the following: exploratory data analysis, preprocessing and normalization of data, text wrangling, dimensionality reduction, anomaly detection, rare event modeling, statistical analysis, big data manipulation, language modeling, word embeddings, machine learning pipeline architecture

comprehensive health care coverage and emotional well-being support
market-leading retirement
generous paid time off and parental leave
charitable giving employee match program
educational assistance including student loan repayment, tuition reimbursement, and learning resources to develop your career